In recent years, the notion of 'one gene makes one protein that functions in one signaling pathway' in mammalian cells has been shown to be overly simplistic. Recent evidence suggests that more than 50% of the human genes produce multiple protein isoforms, through alternative splicing and alternative usage of transcription initiation and/or termination. Notably, the disruption of many of these genes is implicated in cancer and several neuropsychiatric disorders. For majority of human genes the resulting multiple protein isoforms are functionally different and can participate in different signaling pathways. However, nearly after a decade since the completion of the human genome draft sequence, we still assume 'gene' as the basic functional unit in a cell. We argue that the isoform-level gene products - 'transcript variants' and 'protein isoforms' are the basic functionalunits in a mammalian cell, and accordingly, the informatics resources for managing and analyzing gene regulation data in mammalian cells should adopt 'gene isoform centric' rather than 'gene centric' approaches. We propose to build an informatics platform for understanding gene regulation at isoform-level by developing statically rigorous bioinformatics resources for processing Next-Generation Sequencing (NGS) data. Recently, computational approaches that combine seemingly disparate experimental data have been successful in developing concise gene regulation models and transcriptional modules. We plan to extend these methodologies to perform integrative analysis of multiple high-throughput data sets currently generated across different laboratories, including ours at Wistar, into computational models to predict different transcriptional isoforms of mammalian genes and protein-DNA interactions at isoform level. We will apply innovative statistical modeling approaches that combine state-of-the-art meta-classification algorithms, such as Nave Bayes Tree, Bagging and LogitBoost, with Random Forest feature selection to classify different types of target promoters with good classification accuracy and reduced instability, in order to predict gene promoters and infer the protein-DNA interactions from ChIP-seq data. The computational models and the derived information will be integrated into a novel database, which will serve as an in silico platform for transcriptional regulation studies. This will be completed by pursuing the following aims, (1) develop computational pipelines to identify the orthologous promoters, corresponding transcript variants and protein isoforms that are conserved between human and mouse, (2) develop efficient algorithms and informatics pipelines for integrative analysis of NGS datasets to predict the activity and expression of both known and novel promoters and their transcript variants, in various tissues, developmental stages, and disease conditions, and (3) develop a web-accessible database for integrating the information generated. The development of these methods and user-friendly software will provide useful tools to better understand gene regulatory mechanisms in mammalian cells, and more importantly, how dis-regulation of these mechanisms leads to a variety of diseases.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
7R01LM011297-02
Application #
8823012
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Ye, Jane
Project Start
2013-05-02
Project End
2016-04-30
Budget Start
2014-02-12
Budget End
2014-04-30
Support Year
2
Fiscal Year
2013
Total Cost
$152,050
Indirect Cost
$54,050
Name
Northwestern University Chicago
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
005436803
City
Chicago
State
IL
Country
United States
Zip Code
60611
Calvert, Andrea E; Chalastanis, Alexandra; Wu, Yongfei et al. (2017) Cancer-Associated IDH1 Promotes Growth and Resistance to Targeted Therapies in the Absence of Mutation. Cell Rep 19:1858-1873
Liu, Xianpeng; Zhao, Bo; Sun, Limin et al. (2017) Orthogonal ubiquitin transfer identifies ubiquitination substrates under differential control by the two ubiquitin activating enzymes. Nat Commun 8:14286
Shilpi, Arunima; Bi, Yingtao; Jung, Segun et al. (2017) Identification of Genetic and Epigenetic Variants Associated with Breast Cancer Prognosis by Integrative Bioinformatics Analysis. Cancer Inform 16:1-13
Dapas, Matthew; Kandpal, Manoj; Bi, Yingtao et al. (2017) Comparative evaluation of isoform-level gene expression estimation algorithms for RNA-seq and exon-array platforms. Brief Bioinform 18:260-269
Vannini, Ivan; Wise, Petra M; Challagundla, Kishore B et al. (2017) Transcribed ultraconserved region 339 promotes carcinogenesis by modulating tumor suppressor microRNAs. Nat Commun 8:1801
Malchenko, Sergey; Sredni, Simone Treiger; Bi, Yingtao et al. (2017) Stabilization of HIF-1? and HIF-2?, up-regulation of MYCC and accumulation of stabilized p53 constitute hallmarks of CNS-PNET animal model. PLoS One 12:e0173106
Van Roosbroeck, Katrien; Fanini, Francesca; Setoyama, Tetsuro et al. (2017) Combining Anti-Mir-155 with Chemotherapy for the Treatment of Lung Cancers. Clin Cancer Res 23:2891-2904
Bell, Jonathan B; Eckerdt, Frank D; Alley, Kristen et al. (2016) MNK Inhibition Disrupts Mesenchymal Glioma Stem Cells and Prolongs Survival in a Mouse Model of Glioblastoma. Mol Cancer Res 14:984-993
Jin, Hong-Jian; Jung, Segun; DebRoy, Auditi R et al. (2016) Identification and validation of regulatory SNPs that modulate transcription factor chromatin binding and gene expression in prostate cancer. Oncotarget 7:54616-54626
Macyszyn, Luke; Akbari, Hamed; Pisapia, Jared M et al. (2016) Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro Oncol 18:417-25

Showing the most recent 10 out of 20 publications